A method for determining a physiological age of a subject

Abstract

It is disclosed a computer-implemented method for determining a physiological age of a subject, comprising applying, on a set of values comprising at least values of biological variables relative to the subject, a trained model configured to predict the chronological age of a subject based on the set of values, to obtain a predicted age of the subject, wherein the physiological age corresponds to said predicted age.

1 . A computer-implemented method for determining a physiological age of a subject, comprising applying, on a set of values comprising at least values of biological variables relative to the subject, a trained model configured to predict the chronological age of a subject based on the set of values, to obtain a predicted age of the subject, wherein the physiological age corresponds to said predicted age. 
     
     
         2 . The computer-implemented method according to  claim 1 , further comprising comparing the physiological age of the subject with the chronological age of the subject, wherein a positive difference between the physiological age of the subject and the chronological age is indicative of premature ageing of the subject. 
     
     
         3 . The method according to  claim 1 , further comprising comparing the physiological age of the subject with a reference age corresponding to a mean age predicted by the trained model on a reference population, and when the physiological age of the subject differs from the reference age, identifying biological variables most contributing to the difference. 
     
     
         4 . The computer-implemented method according to  claim 3 , wherein the reference population is a population of individuals having the same chronological age as the individual. 
     
     
         5 . The computer-implemented method according to  claim 3 , wherein identifying the biological variables most contributing to the difference comprises determining SHAP values associated to each of the biological variables and identifying the SHAP values having highest absolute value. 
     
     
         6 . The computer-implemented method according to  claim 3 , further comprising comparing at least one value of a biological variable most contributing to the difference, to a reference value of said biological variable for the same chronological age. 
     
     
         7 . The computer-implemented method according to  claim 6 , wherein the reference value of a biological variable for a given chronological age is determined as a mean value of the biological variable among a plurality of individuals of said given chronological age for which said biological variable does not contribute to a difference between the predicted age and the chronological age. 
     
     
         8 . The computer-implemented method according to  claim 3 , further comprising determining an ageing profile of the subject among a plurality of pre-established ageing profiles, based on the identified biological or physiological values most contributing to the difference. 
     
     
         9 . The computer-implemented method according to  claim 8 , wherein the plurality of pre-established ageing profiles are determined by:
 predicting the chronological age of a plurality of individuals of a population by implementing the trained model, wherein the population comprises for each of a plurality of chronological ages, a plurality of individuals,   determining at least a mean predicted age of the population,   determining, for a plurality of individuals of the population, the SHAP values of the biological variables most contributing to a difference between the predicted age for the individual and the mean predicted age,   performing clustering on the SHAP values to obtain a finite number of clusters, wherein each cluster corresponds to an ageing profile.   
     
     
         10 . The computer-implemented method according to  claim 1 , wherein the trained model is an XGboost model with custom loss function being a function of chronological age. 
     
     
         11 . A non-transitory computer-readable storage medium having stored thereon code instructions which, when executed by a processor, cause said processor to implementing a method according to  claim 1 . 
     
     
         12 . A computing system comprising:
 a processor,   a non-transitory computer-readable medium storing program code that is executable by the processor,   
       wherein the processor is configured for executing the program code to perform operations comprising applying, on a set of values of biological variables relative to the subject, a trained model configured to predict the chronological age of a subject based on the set of values, to obtain a predicted age of the subject, wherein the predicted age of the subject corresponds to a physiological age. 
     
     
         13 . The computing system according to  claim 12 , wherein the processor is communicatively coupled via a data network to a client system, and is configured to receive the set of values of biological variables relative to the subject from the client system and to return to the client system the physiological age of the subject, or a difference between the physiological age of the subject and a reference age corresponding to a mean age predicted by the trained model on a population. 
     
     
         14 . The computing system according to  claim 13 , wherein when the computed difference is different from zero, the processor is further configured to compute SHAP values associated to each of the biological variables and identifying the SHAP values having highest absolute value, said SHAP values corresponding to biological variables most contributing to the computed difference. 
     
     
         15 . The computing system according to  claim 14 , wherein the processor is further configured to generate graphical data representing the SHAP values having highest absolute value, wherein the SHAP values contributing to increasing the age predicted by the model with respect to the reference age are represented in a first color and the SHAP values contributing to decreasing the age predicted by the model with respect to the reference age are represented in a second color.